4 papers
When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts
Chengyuan Yao, Yunxuan Tang, Christopher Brooks +2
Predictive models are typically trained on historical data to predict future outcomes. While it is commonly assumed that training on more historical data would improve model perfor…
Towards Fair and Privacy-Aware Transfer Learning for Educational Predictive Modeling: A Case Study on Retention Prediction in Community Colleges
Chengyuan Yao, Carmen Cortez, Renzhe Yu
Predictive analytics is widely used in learning analytics, but many resource-constrained institutions lack the capacity to develop their own models or rely on proprietary ones trai…
The Life Cycle of Large Language Models: A Review of Biases in Education
Jinsook Lee, Yann Hicke, Renzhe Yu +2
Large Language Models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM-based appl…
Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift
Nicolas Acevedo, Carmen Cortez, Chris Brooks +2
Distribution shift is a common situation in machine learning tasks, where the data used for training a model is different from the data the model is applied to in the real world. T…